Big Data Analytics Certificate
Traditional methods of capturing, analyzing, and storing data have become inadequate for processing the massive amounts of data produced by today’s global businesses and organizations. Financial markets, social media sites, web transactions, GIS sensors, and similar applications can produce data sets that have outpaced the capacity of ordinary statistical packages and relational databases. Many organizations store a wealth of data that they simply cannot process fast enough in order to utilize the information as a basis for timely business decisions.
Big Data tools, on the other hand, have the capability of processing zettabytes (a trillion gigabytes) of data, which may be at rest or in motion, and may be structured or unstructured data. Big Data tools give a variety of types of organizations the capacity to analyze huge quantities of data, arriving in high volume, and to make decisions based on that data in real time.
This certificate is designed to provide students with the tools necessary to compete in the Big Data space. Students will use currently available big data tools to capture, analyze, and present big data. They will explore a variety of applications where Big Data tools can be applied, and they will complete a Big Data project.
Summary of Certificate in Big Data Analytics
Required Courses (6 credits)
BMIS 326 Introduction to Data Analytics 3 cr. This course introduces the terminology and application of big data and data analytics. Students will complete cases in a variety of disciplines as they become acquainted with some of the software, tools and techniques of data analytics, including introductions to Python, R, Hadoop, and Tableau.
BMIS 482 Big Data Project 3 cr. Students will work in cross-disciplinary teams to complete big data projects from several different disciplines. Lectures will provide background on appropriate methods where needed. In addition to big data topics, students will explore the topic of project management.
M 467 Data Analytics Projects 3 cr. This is a practicum course aimed at developing skills needed to solve big data problems facing in industry and academics. Problems are brought to the class by local technology-oriented businesses and university researchers. Lecture topics include project management, interacting with clients, and written and oral presentation of results. Additional lecture topics will be selected to address the specific problems brought to the class and may cover data reduction methods, algorithm design and predictive analytics. Prerequisites are two mathematics classes at the 200 level or above and two courses that earn credit toward the University of Montana Big Data Certificate.
Elective Courses (6 credits required)
CSCI 444 Data Visualization 3 cr. Offered intermittently. Prereq., M 171; programming experience; and junior, senior, or graduate status; or consent of instr. Visualization fundamentals and applications using special visualization software; formulation of 3-D empirical models; translation of 3-D models into graphical displays; time sequences and pseudo-animation; interactive versus presentation techniques; special techniques for video, CD and other media.
CSCI 447 - Machine Learning 3 cr. Offered intermittently. Prereq., CSCI 232 or consent of instr. Introduction to the framework of learning from examples, various learning algorithms such as neural networks, and generic learning principles such as inductive bias, Occam's Razor, and data mining. Credit not allowed for both CSCI 447 and CSCI 547. Course Attributes: Co-Convened Course
CSCI 448 - Pattern Recognition 3 cr. Offered intermittently. Prereq., Junior or Senior status. Introduction to the framework of unsupervised learning techniques such as clustering (agglomerative, fuzzy, graph theory based, etc.), multivariate analysis approaches (PCA, MDS, LDA, etc.), image analysis (edge detection, etc.), as well as feature selection and generation. Emphasis will be on the underlying algorithms and their implementation. Credit not allowed for both CSCI 448 and CSCI 548. Course Attributes: Co-Convened Course
U CSCI 464/564 Applications of Mining Big Data 3 cr. Offered intermittently. Prereq., upper division or consent of instr. Co-convenes with CSCI 564. Introduction to existing data mining software systems and their use, with focus on practical exercises. Topics include data acquisition, data cleansing, feature selection, and data analysis. Credit not allowed for both CSCI 464 and CSCI 564.
CSCI 480/580 Applied Parallel Computing Techniques 3 cr. Offered intermittently. Prereq., CSCI 205 and 232, of instructor consent. Co-convenes with CSCI 580. This course is an introduction to parallelism and parallel programming. Topics include the various forms of parallelism on modern computer hardware (e.g. SIMD vector instructions, GPUs, multiple cores, and networked clusters), with coverage of locality and latency, shared vs non-shared memory, and synchronization mechanisms (locking, atomicity, etc). Assignments will include significant parallel programming projects. Credit not allowed for both CSCI 480 and CSCI 580
BMIS 465 Real-Time Data Analytics 3 cr. Students will use IBM Infosphere Streams software to extract relevant data from data sources in motion, and they will analyze the data using appropriate statistical and mathematical techniques. Applications include cybersecurity, finance, social media, marketing, and others
BMKT 440 Marketing Analytics 3 cr. Students will understand the application of advanced digital marketing practices to identify valuable business opportunities from the data. They will be introduced to analytical methods such as forecasting, predictive analytics, data mining, decision tree models, and web analytics. They will analyze and apply data to generate marketing strategies. They will also learn how to communicate results-focused marketing strategies to clients.
M 461 Practical Big Data Analysis 3 cr. M 461 is a methods course supporting the Big Data Certificate Program. The course provides the students with the essential tools for the analysis of big data. The content consists of map reduce and canonical information methods for analyzing massively large data sets, windowing methods for the analysis of streaming data, an introduction to predictive analytics, and an introduction to data visualization methods. Prerequisites are 1. two mathematics classes at the 200 level or above, and 2. one course in probability or statistics at the 300 level or above.
M 462 Theoretical Big Data Analysis 3 cr. Prerequisites: consent of instructor; or M221 Introduction to Linear Algebra and two other Mathematics / Statistics classes at 200-level or above. The main goal of this course is to provide students with a unique opportunity to acquire conceptual knowledge and theoretical background behind mathematical tools applicable to Big Data Analytics and Real Time Computations. Specific challenges of Big Data Analytics, e.g., problems of extracting, unifying, updating, and merging information, and processing of highly parallel and distributed data, will be reviewed. The tools for Big Data Analytics, such as regression analysis, linear estimation, calibration problems, real time processing of incoming (potentially infinite) data, will be studied in more detail. It will be shown how these approaches can be transformed to conform to the Big Data demands.